---
title: "Comparison: TensorZero vs. Langfuse"
sidebarTitle: "Langfuse"
description: "TensorZero is an open-source alternative to Langfuse featuring an LLM gateway, observability, optimization, evaluations, and experimentation."
---

TensorZero and Langfuse both provide open-source tools that streamline LLM engineering workflows.
TensorZero focuses on inference and optimization, while Langfuse specializes in powerful interfaces for observability and evals.
That said, **you can get the best of both worlds by using TensorZero alongside Langfuse**.

## Similarities

- **Open Source & Self-Hosted.**
  Both TensorZero and Langfuse are open source and self-hosted.
  Your data never leaves your infrastructure, and you don't risk downtime by relying on external APIs.
  TensorZero is fully open-source, whereas Langfuse gates some of its features behind a paid license.

- **Built-in Observability.**
  Both TensorZero and Langfuse offer built-in observability features, collecting inference in your own database.
  Langfuse offers a broader set of advanced observability features, including application-level tracing.
  TensorZero focuses more on structured data collection for optimization, including downstream metrics and feedback.

- **Built-in Evaluations.**
  Both TensorZero and Langfuse offer built-in evaluations features, enabling you to sanity check and benchmark the performance of your prompts, models, and more &mdash; using heuristics and LLM judges.
  TensorZero LLM judges are also TensorZero functions, which means you can optimize them using TensorZero's optimization recipes.
  Langfuse offers a broader set of built-in heuristics and UI features for evaluations.<br />
  [→ TensorZero Evaluations Overview](/evaluations/)

## Key Differences

### TensorZero

- **Unified Inference API.**
  TensorZero offers a unified inference API that allows you to access LLMs from most major model providers with a single integration, with support for structured outputs, tool use, streaming, and more.
  Langfuse doesn't provide a built-in LLM gateway.<br />
  [→ TensorZero Gateway Quickstart](/quickstart/)

- **Built-in Inference-Time Optimizations.**
  TensorZero offers built-in inference-time optimizations (e.g. dynamic in-context learning), allowing you to optimize your inference performance.
  Langfuse doesn't offer any inference-time optimizations.<br />
  [→ Inference-Time Optimizations with TensorZero](/gateway/guides/inference-time-optimizations/)

- **Optimization Recipes.**
  TensorZero offers optimization recipes (e.g. supervised fine-tuning, RLHF, MIPRO) that leverage your own data to improve your LLM's performance.
  Langfuse doesn't offer built-in features like this.<br />
  [→ Optimization Recipes with TensorZero](/recipes/)

- **Automatic Fallbacks for Higher Reliability.**
  TensorZero offers automatic fallbacks to increase reliability.
  Langfuse doesn't offer any such features.<br />
  [→ Retries & Fallbacks with TensorZero](/gateway/guides/retries-fallbacks/)

- **Automated Experimentation (A/B Testing).**
  TensorZero offers built-in experimentation features, allowing you to run experiments on your prompts, models, and inference strategies.
  Langfuse doesn't offer any experimentation features.<br />
  [→ Run adaptive A/B tests with TensorZero](/experimentation/run-adaptive-ab-tests/)

### Langfuse

- **Advanced Observability & Evaluations.**
  While both TensorZero and Langfuse offer observability and evaluations features, Langfuse takes it further with advanced observability features.
  Additionally, Langfuse offers a prompt playground, which TensorZero doesn't offer (coming soon!).

- **Access Control.**
  Langfuse offers access control features like SSO and user management.
  TensorZero supports TensorZero API key for inference, but more advanced access control requires complementary tools like Nginx or OAuth2 Proxy.
  [→ Set up auth for TensorZero](/operations/set-up-auth-for-tensorzero)

- **Managed Service.**
  Langfuse offers a paid managed (hosted) service in addition to the open-source version.
  TensorZero is fully open-source and self-hosted.

<Tip title="Feedback">

Is TensorZero missing any features that are really important to you? Let us know on [GitHub Discussions](https://github.com/tensorzero/tensorzero/discussions), [Slack](https://www.tensorzero.com/slack), or [Discord](https://www.tensorzero.com/discord).

</Tip>

## Combining TensorZero and Langfuse

You can combine TensorZero and Langfuse to get the best of both worlds.

A leading voice agent startup uses TensorZero for inference and optimization, alongside Langfuse for more advanced observability and evals.
